Visual Computing Design for Explainable, Spatially-aware Machine Learning

Many real-world machine learning applications require combining the strengths of human analysts who have specialized knowledge, with computational systems that excel at statistical analysis and large-scale data processing. This is especially true in the case of statistical modeling with spatial data, where information about the coordinates of each data item benefits from domain expert knowledge. For example, in many cancers, the location of the tumors and the specific pattern in which the disease spreads have an impact on the patient's treatment, and the specific organs nearby that are affected can impact long-term patient outcomes. However, the reliance on retrospective cohorts and the fact that organs with distinct functions are spatially adjacent can lead to many spurious findings when using traditional statistical approaches. This dissertation attempts to deal with these issues through the design of integrated visual computing and explainable machine-learning tools with spatial data, with a focus on human-centered co-design of these systems alongside clients. The specific research question this dissertation aims is: How do we integrate spatial data into explainable visual computing + machine learning (VC+ML) systems? This involves several sub-challenges: 1) Domain characterization; 2) strategies for the design of models for spatial VC problems that consider requirements from both ML model builders and model clients in a collaborative setting; 3) modeling similarity between sets of spatial features; 4) the design visual encodings for to explain these spatial machine learning model predictions to clients; and 5) approaches to measure the effect of deploying these spatial VC+ML systems in practice. To answer this, I will detail five design studies focused on human-centered collaborative design of mixed VC + ML systems for real work problems that use spatial data, followed by general design insights for the design of these systems.